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genomics-variant-annotation

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Load when summarising functional impact of an annotated variant CSV — per-IMPACT counts (HIGH / MODERATE / LOW / MODIFIER), top consequences, gene-affected count. Skip when input is a raw VCF (convert with `bcftools +split-vep` first), when calling raw variants (use `genomics-variant-calling`), or filtering VCFs (use `genomics-vcf-operations`).

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How to use this skill

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Source SKILL.md: https://github.com/TianGzlab/OmicsClaw/blob/HEAD/skills/genomics/genomics-variant-annotation/SKILL.md

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genomics-variant-annotation

When to use

The user has a CSV containing per-variant annotations (lowercase columns chrom, pos, ref, alt, consequence, impact, gene, optionally cadd_phred) — typically the output of running VEP, snpEff, or ANNOVAR upstream and exporting the resulting VCF to CSV (e.g. via bcftools +split-vep). This skill computes per-IMPACT counts, top consequences, and the count of distinct genes affected.

The script does NOT run VEP / snpEff / ANNOVAR, and does NOT parse a raw VCF — it only reads CSV. For raw calling use genomics-variant-calling; for VCF filtering use genomics-vcf-operations.

Inputs & Outputs

InputFormatRequired
Annotated CSV.csv with lowercase columns chrom, pos, ref, alt, consequence, impact, gene (and optionally cadd_phred)yes (unless --demo)
OutputPathNotes
Annotated tabletables/annotated_variants.csvper-variant copy of the input CSV
Impact distributiontables/impact_distribution.csvcounts per IMPACT class
Reportreport.md + result.jsonresult.json["data"]["top_consequences"] mirrors top-N consequence counts

Flow

  1. Load CSV (--input <annotated.csv>) or generate a demo annotated CSV at output_dir/demo_annotated_variants.csv with --n-variants records (variant_annotation.py:227).
  2. Read columns directly via pd.read_csv (variant_annotation.py:356) — no VCF / VEP / snpEff parser exists in this skill.
  3. Aggregate per-IMPACT counts (variant_annotation.py:240); pick top-N consequences (:241); count distinct genes touched (:252).
  4. Write tables/annotated_variants.csv (variant_annotation.py:366) + tables/impact_distribution.csv (:377) + report.md + result.json (:383).

Gotchas

  • CSV-only — no VCF parser exists. variant_annotation.py:356 is pd.read_csv(input_path); passing a .vcf raises ValueError("Could not parse input file: ...") at variant_annotation.py:358. Convert VCFs to CSV first with bcftools +split-vep -d -f '%CHROM,%POS,%REF,%ALT,%CSQ\n' and post-process to the required column names.
  • Required CSV columns are LOWERCASE. Code reads df["impact"] (:240), df["consequence"] (:241), df["gene"] (:252), and optionally df["cadd_phred"] (:271). A CSV with IMPACT / Consequence / Gene raises KeyError.
  • --input REQUIRED unless --demo. variant_annotation.py:348 raises ValueError("--input required when not using --demo"); non-existent paths raise FileNotFoundError at :351.
  • No annotator is invoked. This skill consumes an already-annotated CSV — it does NOT run VEP / snpEff / ANNOVAR. Run an annotator upstream and convert its output to CSV.
  • CADD scoring is optional. When cadd_phred is absent the report omits the CADD section; do NOT add a placeholder NaN column or the value-counts will mis-render.
  • Demo CSV uses fixed IMPACT proportions (~10% HIGH, 30% MODERATE, 50% LOW, 10% MODIFIER). Useful for orchestrator smoke tests; not biologically meaningful.

Key CLI

# Demo
python omicsclaw.py run genomics-variant-annotation --demo --output /tmp/anno_demo

# Real annotated CSV (lowercase columns)
python omicsclaw.py run genomics-variant-annotation \
  --input my_annotations.csv --output results/

See also

  • references/parameters.md — every CLI flag
  • references/methodology.md — VEP / snpEff / ANNOVAR field semantics, IMPACT taxonomy
  • references/output_contract.md — tables/annotated_variants.csv + impact distribution
  • Adjacent skills: genomics-variant-calling (upstream — produces raw VCF), genomics-vcf-operations (upstream — filtering / normalisation before annotation), genomics-sv-detection (parallel — structural variants), genomics-phasing (parallel — phasing analysis)